Complementary‐Relationship‐Based Modeling of Terrestrial Evapotranspiration Across China During 1982–2012: Validations and Spatiotemporal Analyses

Abstract

Having recognized the limitations in spatial representativeness and/or temporal coverage of (i) current ground ETa observations and (ii) land surface model‐ and remote sensing‐based ETa estimates due to uncertainties in soil and vegetation parameters, a calibration‐free nonlinear complementary relationship (CR) model is employed with inputs of air and dew‐point temperature, wind speed, and net radiation to estimate 0.1°, monthly ETa over China during 1982–2012. The modeled ETa rates were first validated against 13 eddy‐covariance measurements, producing Nash‐Sutcliffe efficiency values in the range of 0.72–0.94. On the basin scale, the modeled ETa values yielded a relative bias of 6%, and a Nash‐Sutcliffe efficiency value of 0.80 in comparison with water‐balance‐derived evapotranspiration rates across 10 major river basins in China, indicating the CR‐simulated ETa rates reliable over China. Further evaluations suggest that the CR‐based ETa product is more accurate than seven other mainstream ETa products. The 31‐year mean annual ETa value decreases from the southeast to the northwest in China, resulting in a country average of 406 ± 15 mm/year. The country‐representative annual ETa rates slightly decreased with a rate of −0.5 mm/year (p = 0.86) during 1982–2012. Annual ETa increased significantly over most parts of western and northeastern China but decreased significantly in many regions of the North China Plain as well as in the eastern and southern coastal regions. The present CR‐based method, with its calibration‐free nature and minimal data requirement, could help future calibrations/verifications of the more complex and more data‐intensive land surface model‐ and remote sensing‐based models.

The Global Land Surface Satellite (GLASS) Product Suite

“…ET (Ma et al 2019), compare with the retrievals of a new inversion algorithm as a reference value (Ma et al 2018), and to study climate impacts due to the widespread deployment of utility-scale solar energy installations (Li et al 2017).…”

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